Overview

Brought to you by YData

Dataset statistics

Number of variables59
Number of observations581012
Missing cells298
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory288.7 MiB
Average record size in memory521.0 B

Variable types

Numeric14
Categorical45

Alerts

Observation_ID has constant value "1" Constant
Aspect is highly overall correlated with Facet and 1 other fieldsHigh correlation
Cover_Type is highly overall correlated with Wilderness_Area4High correlation
Elevation is highly overall correlated with Soil_Type10 and 2 other fieldsHigh correlation
Facet is highly overall correlated with Aspect and 1 other fieldsHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 3 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Hillshade_3pmHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Vertical_Distance_To_HydrologyHigh correlation
Soil_Type10 is highly overall correlated with ElevationHigh correlation
Soil_Type29 is highly overall correlated with Wilderness_Area1High correlation
Soil_Type40 is highly overall correlated with ElevationHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Horizontal_Distance_To_HydrologyHigh correlation
Wilderness_Area1 is highly overall correlated with Soil_Type29 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly overall correlated with Wilderness_Area1High correlation
Wilderness_Area4 is highly overall correlated with Cover_Type and 1 other fieldsHigh correlation
Wilderness_Area2 is highly imbalanced (70.8%) Imbalance
Wilderness_Area4 is highly imbalanced (65.8%) Imbalance
Soil_Type1 is highly imbalanced (95.3%) Imbalance
Soil_Type2 is highly imbalanced (90.0%) Imbalance
Soil_Type3 is highly imbalanced (93.1%) Imbalance
Soil_Type4 is highly imbalanced (85.1%) Imbalance
Soil_Type5 is highly imbalanced (97.3%) Imbalance
Soil_Type6 is highly imbalanced (91.1%) Imbalance
Soil_Type7 is highly imbalanced (99.7%) Imbalance
Soil_Type8 is highly imbalanced (99.6%) Imbalance
Soil_Type9 is highly imbalanced (97.9%) Imbalance
Soil_Type10 is highly imbalanced (68.8%) Imbalance
Soil_Type11 is highly imbalanced (85.1%) Imbalance
Soil_Type12 is highly imbalanced (70.7%) Imbalance
Soil_Type13 is highly imbalanced (80.6%) Imbalance
Soil_Type14 is highly imbalanced (98.8%) Imbalance
Soil_Type15 is highly imbalanced (> 99.9%) Imbalance
Soil_Type16 is highly imbalanced (95.5%) Imbalance
Soil_Type17 is highly imbalanced (94.8%) Imbalance
Soil_Type18 is highly imbalanced (96.8%) Imbalance
Soil_Type19 is highly imbalanced (94.0%) Imbalance
Soil_Type20 is highly imbalanced (88.2%) Imbalance
Soil_Type21 is highly imbalanced (98.4%) Imbalance
Soil_Type22 is highly imbalanced (68.3%) Imbalance
Soil_Type23 is highly imbalanced (53.3%) Imbalance
Soil_Type24 is highly imbalanced (77.3%) Imbalance
Soil_Type25 is highly imbalanced (99.0%) Imbalance
Soil_Type26 is highly imbalanced (95.9%) Imbalance
Soil_Type27 is highly imbalanced (98.0%) Imbalance
Soil_Type28 is highly imbalanced (98.3%) Imbalance
Soil_Type30 is highly imbalanced (70.5%) Imbalance
Soil_Type31 is highly imbalanced (73.9%) Imbalance
Soil_Type32 is highly imbalanced (56.2%) Imbalance
Soil_Type33 is highly imbalanced (60.6%) Imbalance
Soil_Type34 is highly imbalanced (97.2%) Imbalance
Soil_Type35 is highly imbalanced (96.8%) Imbalance
Soil_Type36 is highly imbalanced (99.7%) Imbalance
Soil_Type37 is highly imbalanced (99.4%) Imbalance
Soil_Type38 is highly imbalanced (82.2%) Imbalance
Soil_Type39 is highly imbalanced (83.8%) Imbalance
Soil_Type40 is highly imbalanced (88.7%) Imbalance
Horizontal_Distance_To_Hydrology is highly skewed (γ1 = 257.2463674) Skewed
Water_Level is uniformly distributed Uniform
Water_Level has unique values Unique
Horizontal_Distance_To_Hydrology has 24603 (4.2%) zeros Zeros
Vertical_Distance_To_Hydrology has 38665 (6.7%) zeros Zeros

Reproduction

Analysis started2025-08-24 14:44:28.312852
Analysis finished2025-08-24 14:47:00.314745
Duration2 minutes and 32 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3270098.7
Minimum2054195
Maximum4263090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:00.973307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2054195
5-th percentile2658630
Q13103945
median3310580
Q33495115
95-th percentile3686280
Maximum4263090
Range2208895
Interquartile range (IQR)391170

Descriptive statistics

Standard deviation309383.13
Coefficient of variation (CV)0.094609724
Kurtosis0.74925078
Mean3270098.7
Median Absolute Deviation (MAD)193375
Skewness-0.81759582
Sum1.8999666 × 1012
Variance9.5717922 × 1010
MonotonicityNot monotonic
2025-08-24T16:47:01.826406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3279640 1681
 
0.3%
3273010 1674
 
0.3%
3305055 1671
 
0.3%
3284060 1662
 
0.3%
3287375 1656
 
0.3%
3290690 1656
 
0.3%
3301740 1619
 
0.3%
3265275 1590
 
0.3%
3261960 1577
 
0.3%
3276325 1571
 
0.3%
Other values (1968) 564655
97.2%
ValueCountFrequency (%)
2054195 1
 
< 0.1%
2055300 1
 
< 0.1%
2056405 1
 
< 0.1%
2058615 1
 
< 0.1%
2061930 1
 
< 0.1%
2063035 1
 
< 0.1%
2064140 1
 
< 0.1%
2067455 3
< 0.1%
2068560 4
< 0.1%
2069665 1
 
< 0.1%
ValueCountFrequency (%)
4263090 2
 
< 0.1%
4261985 1
 
< 0.1%
4260880 1
 
< 0.1%
4257565 1
 
< 0.1%
4256460 1
 
< 0.1%
4255355 2
 
< 0.1%
4254250 1
 
< 0.1%
4253145 4
< 0.1%
4252040 1
 
< 0.1%
4249830 6
< 0.1%

Aspect
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.65681
Minimum0
Maximum360
Zeros4914
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:03.025962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median127
Q3260
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)202

Descriptive statistics

Standard deviation111.91372
Coefficient of variation (CV)0.71897736
Kurtosis-1.2202389
Mean155.65681
Median Absolute Deviation (MAD)85
Skewness0.40262832
Sum90438473
Variance12524.681
MonotonicityNot monotonic
2025-08-24T16:47:03.484807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 6308
 
1.1%
0 4914
 
0.8%
90 4677
 
0.8%
135 3834
 
0.7%
63 3680
 
0.6%
315 3574
 
0.6%
72 3407
 
0.6%
18 3403
 
0.6%
27 3392
 
0.6%
34 2836
 
0.5%
Other values (351) 540987
93.1%
ValueCountFrequency (%)
0 4914
0.8%
1 1671
 
0.3%
2 1902
 
0.3%
3 1945
 
0.3%
4 2267
0.4%
5 2063
0.4%
6 2242
0.4%
7 2194
0.4%
8 2213
0.4%
9 2460
0.4%
ValueCountFrequency (%)
360 51
 
< 0.1%
359 1407
0.2%
358 1749
0.3%
357 1860
0.3%
356 2025
0.3%
355 1933
0.3%
354 2025
0.3%
353 1946
0.3%
352 1985
0.3%
351 2184
0.4%

Facet
Real number (ℝ)

High correlation 

Distinct576099
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean389.91933
Minimum0
Maximum903.4134
Zeros4914
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:03.983579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.03693
Q1145.49094
median318.1596
Q3652.52898
95-th percentile862.16162
Maximum903.4134
Range903.4134
Interquartile range (IQR)507.03804

Descriptive statistics

Standard deviation280.3433
Coefficient of variation (CV)0.71897768
Kurtosis-1.2202119
Mean389.91933
Median Absolute Deviation (MAD)212.92547
Skewness0.40263415
Sum2.2654781 × 108
Variance78592.364
MonotonicityNot monotonic
2025-08-24T16:47:04.249678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4914
 
0.8%
415.3947273 1
 
< 0.1%
743.2826683 1
 
< 0.1%
170.1050557 1
 
< 0.1%
664.9235964 1
 
< 0.1%
290.3332007 1
 
< 0.1%
745.1493799 1
 
< 0.1%
639.9159642 1
 
< 0.1%
90.18197283 1
 
< 0.1%
225.2044342 1
 
< 0.1%
Other values (576089) 576089
99.2%
ValueCountFrequency (%)
0 4914
0.8%
2.500012958 1
 
< 0.1%
2.500013004 1
 
< 0.1%
2.500035936 1
 
< 0.1%
2.50004174 1
 
< 0.1%
2.500046905 1
 
< 0.1%
2.500047598 1
 
< 0.1%
2.500056787 1
 
< 0.1%
2.50005712 1
 
< 0.1%
2.500065919 1
 
< 0.1%
ValueCountFrequency (%)
903.4134047 1
< 0.1%
903.2815138 1
< 0.1%
903.251869 1
< 0.1%
903.1885038 1
< 0.1%
903.0476497 1
< 0.1%
902.9809444 1
< 0.1%
902.9704777 1
< 0.1%
902.9428854 1
< 0.1%
902.9014565 1
< 0.1%
902.8800042 1
< 0.1%

Slope
Real number (ℝ)

Distinct67
Distinct (%)< 0.1%
Missing298
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean14.103738
Minimum0
Maximum66
Zeros654
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:04.669140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4880583
Coefficient of variation (CV)0.53092722
Kurtosis0.58163759
Mean14.103738
Median Absolute Deviation (MAD)5
Skewness0.78940778
Sum8190238
Variance56.071017
MonotonicityNot monotonic
2025-08-24T16:47:05.124144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 33812
 
5.8%
10 33795
 
5.8%
12 33203
 
5.7%
13 32401
 
5.6%
9 32031
 
5.5%
14 30265
 
5.2%
8 30120
 
5.2%
15 29116
 
5.0%
16 26527
 
4.6%
7 26381
 
4.5%
Other values (57) 273063
47.0%
ValueCountFrequency (%)
0 654
 
0.1%
1 3678
 
0.6%
2 7722
 
1.3%
3 11608
 
2.0%
4 16329
2.8%
5 20799
3.6%
6 24497
4.2%
7 26381
4.5%
8 30120
5.2%
9 32031
5.5%
ValueCountFrequency (%)
66 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
 
< 0.1%
61 4
< 0.1%
60 2
 
< 0.1%
59 3
< 0.1%
58 1
 
< 0.1%
57 7
< 0.1%

Inclination
Real number (ℝ)

Distinct580946
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00048448433
Minimum-0.9999989
Maximum0.99999198
Zeros0
Zeros (%)0.0%
Negative290798
Negative (%)50.1%
Memory size4.4 MiB
2025-08-24T16:47:06.010407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9999989
5-th percentile-0.90118775
Q1-0.50053864
median-0.0010618423
Q30.50093182
95-th percentile0.90000196
Maximum0.99999198
Range1.9999909
Interquartile range (IQR)1.0014705

Descriptive statistics

Standard deviation0.57774126
Coefficient of variation (CV)-1192.487
Kurtosis-1.1997547
Mean-0.00048448433
Median Absolute Deviation (MAD)0.50073573
Skewness0.00041076011
Sum-281.49121
Variance0.33378497
MonotonicityNot monotonic
2025-08-24T16:47:06.729033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7189573141 2
 
< 0.1%
0.3328840925 2
 
< 0.1%
0.3246810843 2
 
< 0.1%
0.6674533876 2
 
< 0.1%
-0.6796830481 2
 
< 0.1%
0.5623269489 2
 
< 0.1%
-0.9452520562 2
 
< 0.1%
-0.7973131387 2
 
< 0.1%
-0.4832314763 2
 
< 0.1%
0.2549231719 2
 
< 0.1%
Other values (580936) 580992
> 99.9%
ValueCountFrequency (%)
-0.9999988982 1
< 0.1%
-0.9999976484 1
< 0.1%
-0.9999907063 1
< 0.1%
-0.9999894733 1
< 0.1%
-0.9999793936 1
< 0.1%
-0.9999791123 1
< 0.1%
-0.9999779249 1
< 0.1%
-0.9999747444 1
< 0.1%
-0.9999715462 1
< 0.1%
-0.9999624621 1
< 0.1%
ValueCountFrequency (%)
0.9999919822 1
< 0.1%
0.9999820655 1
< 0.1%
0.9999806052 1
< 0.1%
0.9999805372 1
< 0.1%
0.9999761973 1
< 0.1%
0.9999753498 1
< 0.1%
0.9999703011 1
< 0.1%
0.9999667071 1
< 0.1%
0.9999649487 1
< 0.1%
0.9999518534 1
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct576
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5062.4858
Minimum0
Maximum3.7428991 × 108
Zeros24603
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:07.471482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median218
Q3384
95-th percentile685
Maximum3.7428991 × 108
Range3.7428991 × 108
Interquartile range (IQR)276

Descriptive statistics

Standard deviation952330.84
Coefficient of variation (CV)188.11526
Kurtosis76674.572
Mean5062.4858
Median Absolute Deviation (MAD)133
Skewness257.24637
Sum2.941365 × 109
Variance9.0693402 × 1011
MonotonicityNot monotonic
2025-08-24T16:47:07.768358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 34138
 
5.9%
0 24603
 
4.2%
150 20785
 
3.6%
60 19186
 
3.3%
67 15223
 
2.6%
42 14646
 
2.5%
108 14358
 
2.5%
85 13741
 
2.4%
90 11140
 
1.9%
120 10672
 
1.8%
Other values (566) 402520
69.3%
ValueCountFrequency (%)
0 24603
4.2%
30 34138
5.9%
42 14646
2.5%
60 19186
3.3%
67 15223
2.6%
85 13741
2.4%
90 11140
 
1.9%
95 9216
 
1.6%
108 14358
2.5%
120 10672
 
1.8%
ValueCountFrequency (%)
374289909 1
< 0.1%
287854935 1
< 0.1%
228574229 1
< 0.1%
223332556 1
< 0.1%
220431974 1
< 0.1%
178181733 1
< 0.1%
146068820 1
< 0.1%
128352035 1
< 0.1%
119278084 1
< 0.1%
117029271 1
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct700
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.418855
Minimum-173
Maximum601
Zeros38665
Zeros (%)6.7%
Negative55143
Negative (%)9.5%
Memory size4.4 MiB
2025-08-24T16:47:08.022368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-173
5-th percentile-8
Q17
median30
Q369
95-th percentile165
Maximum601
Range774
Interquartile range (IQR)62

Descriptive statistics

Standard deviation58.295232
Coefficient of variation (CV)1.2558524
Kurtosis5.2502958
Mean46.418855
Median Absolute Deviation (MAD)27
Skewness1.7902497
Sum26969912
Variance3398.334
MonotonicityNot monotonic
2025-08-24T16:47:08.510047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38665
 
6.7%
3 9298
 
1.6%
10 8863
 
1.5%
7 8741
 
1.5%
6 8590
 
1.5%
13 8474
 
1.5%
4 8397
 
1.4%
5 7614
 
1.3%
16 7429
 
1.3%
9 7331
 
1.3%
Other values (690) 467610
80.5%
ValueCountFrequency (%)
-173 1
 
< 0.1%
-166 2
< 0.1%
-164 1
 
< 0.1%
-163 1
 
< 0.1%
-161 1
 
< 0.1%
-159 3
< 0.1%
-158 1
 
< 0.1%
-157 2
< 0.1%
-156 2
< 0.1%
-155 3
< 0.1%
ValueCountFrequency (%)
601 1
 
< 0.1%
599 1
 
< 0.1%
598 2
< 0.1%
597 3
< 0.1%
595 2
< 0.1%
592 1
 
< 0.1%
591 1
 
< 0.1%
590 2
< 0.1%
589 3
< 0.1%
588 3
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

Distinct5785
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2350.1466
Minimum0
Maximum7117
Zeros124
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:09.522858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379
Q11106
median1997
Q33328
95-th percentile5483
Maximum7117
Range7117
Interquartile range (IQR)2222

Descriptive statistics

Standard deviation1559.2549
Coefficient of variation (CV)0.66347132
Kurtosis-0.38371119
Mean2350.1466
Median Absolute Deviation (MAD)1040
Skewness0.71367882
Sum1.3654634 × 109
Variance2431275.7
MonotonicityNot monotonic
2025-08-24T16:47:10.219845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1332
 
0.2%
618 1065
 
0.2%
900 918
 
0.2%
390 914
 
0.2%
1020 900
 
0.2%
990 878
 
0.2%
960 868
 
0.1%
997 859
 
0.1%
750 847
 
0.1%
1140 840
 
0.1%
Other values (5775) 571591
98.4%
ValueCountFrequency (%)
0 124
 
< 0.1%
30 313
0.1%
42 171
 
< 0.1%
60 312
0.1%
67 298
0.1%
85 384
0.1%
90 380
0.1%
95 374
0.1%
108 660
0.1%
120 633
0.1%
ValueCountFrequency (%)
7117 1
< 0.1%
7116 1
< 0.1%
7112 1
< 0.1%
7097 1
< 0.1%
7092 1
< 0.1%
7087 2
< 0.1%
7082 1
< 0.1%
7079 1
< 0.1%
7078 2
< 0.1%
7069 1
< 0.1%

Hillshade_9am
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.14605
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:10.824291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1198
median218
Q3231
95-th percentile246
Maximum254
Range254
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.769889
Coefficient of variation (CV)0.12618613
Kurtosis1.8755177
Mean212.14605
Median Absolute Deviation (MAD)16
Skewness-1.1811467
Sum1.232594 × 108
Variance716.62695
MonotonicityNot monotonic
2025-08-24T16:47:11.127797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 11657
 
2.0%
228 11374
 
2.0%
230 11355
 
2.0%
224 11210
 
1.9%
223 10887
 
1.9%
222 10809
 
1.9%
233 10645
 
1.8%
227 10513
 
1.8%
225 10307
 
1.8%
221 10264
 
1.8%
Other values (197) 471991
81.2%
ValueCountFrequency (%)
0 13
< 0.1%
36 1
 
< 0.1%
46 2
 
< 0.1%
50 1
 
< 0.1%
52 2
 
< 0.1%
53 1
 
< 0.1%
54 4
 
< 0.1%
55 1
 
< 0.1%
56 6
< 0.1%
57 2
 
< 0.1%
ValueCountFrequency (%)
254 1898
 
0.3%
253 2236
0.4%
252 2563
0.4%
251 2968
0.5%
250 3341
0.6%
249 3793
0.7%
248 3955
0.7%
247 4443
0.8%
246 5008
0.9%
245 5530
1.0%

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.31872
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:11.259291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.768697
Coefficient of variation (CV)0.088522348
Kurtosis2.0662108
Mean223.31872
Median Absolute Deviation (MAD)12
Skewness-1.0630563
Sum1.2975085 × 108
Variance390.80139
MonotonicityNot monotonic
2025-08-24T16:47:11.414162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 13696
 
2.4%
231 13666
 
2.4%
233 13297
 
2.3%
229 13271
 
2.3%
230 13258
 
2.3%
234 13047
 
2.2%
227 13020
 
2.2%
223 12989
 
2.2%
226 12953
 
2.2%
225 12928
 
2.2%
Other values (175) 448887
77.3%
ValueCountFrequency (%)
0 5
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
53 2
 
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
68 1
 
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
254 5902
1.0%
253 6300
1.1%
252 7171
1.2%
251 7471
1.3%
250 8028
1.4%
249 7714
1.3%
248 8133
1.4%
247 8874
1.5%
246 8665
1.5%
245 8538
1.5%

Hillshade_3pm
Real number (ℝ)

High correlation 

Distinct255
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.52826
Minimum0
Maximum254
Zeros1338
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:11.562231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q1119
median143
Q3168
95-th percentile204
Maximum254
Range254
Interquartile range (IQR)49

Descriptive statistics

Standard deviation38.274529
Coefficient of variation (CV)0.26853993
Kurtosis0.39844001
Mean142.52826
Median Absolute Deviation (MAD)25
Skewness-0.2770532
Sum82810631
Variance1464.9396
MonotonicityNot monotonic
2025-08-24T16:47:11.846547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 7333
 
1.3%
145 7217
 
1.2%
138 7065
 
1.2%
146 6915
 
1.2%
142 6902
 
1.2%
136 6871
 
1.2%
139 6858
 
1.2%
135 6781
 
1.2%
149 6723
 
1.2%
132 6673
 
1.1%
Other values (245) 511674
88.1%
ValueCountFrequency (%)
0 1338
0.2%
1 15
 
< 0.1%
2 15
 
< 0.1%
3 15
 
< 0.1%
4 20
 
< 0.1%
5 18
 
< 0.1%
6 26
 
< 0.1%
7 30
 
< 0.1%
8 21
 
< 0.1%
9 33
 
< 0.1%
ValueCountFrequency (%)
254 4
 
< 0.1%
253 8
 
< 0.1%
252 16
 
< 0.1%
251 11
 
< 0.1%
250 17
 
< 0.1%
249 37
< 0.1%
248 44
< 0.1%
247 61
< 0.1%
246 72
< 0.1%
245 85
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

Distinct5827
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1980.2912
Minimum0
Maximum7173
Zeros51
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:12.164469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile418
Q11024
median1710
Q32550
95-th percentile4944
Maximum7173
Range7173
Interquartile range (IQR)1526

Descriptive statistics

Standard deviation1324.1952
Coefficient of variation (CV)0.66868711
Kurtosis1.6458068
Mean1980.2912
Median Absolute Deviation (MAD)750
Skewness1.2886441
Sum1.150573 × 109
Variance1753493
MonotonicityNot monotonic
2025-08-24T16:47:12.366572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 1412
 
0.2%
541 1099
 
0.2%
607 1054
 
0.2%
942 1023
 
0.2%
997 1004
 
0.2%
700 958
 
0.2%
900 937
 
0.2%
726 923
 
0.2%
752 910
 
0.2%
960 908
 
0.2%
Other values (5817) 570784
98.2%
ValueCountFrequency (%)
0 51
 
< 0.1%
30 206
< 0.1%
42 207
< 0.1%
60 206
< 0.1%
67 416
0.1%
85 207
< 0.1%
90 204
< 0.1%
95 412
0.1%
108 412
0.1%
120 204
< 0.1%
ValueCountFrequency (%)
7173 1
< 0.1%
7172 1
< 0.1%
7168 1
< 0.1%
7150 1
< 0.1%
7145 1
< 0.1%
7142 1
< 0.1%
7141 2
< 0.1%
7140 1
< 0.1%
7131 1
< 0.1%
7126 1
< 0.1%

Wilderness_Area1
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
320216 
1
260796 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Length

2025-08-24T16:47:12.914921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:14.063134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring characters

ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 320216
55.1%
1 260796
44.9%

Wilderness_Area2
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
551128 
1
 
29884

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Length

2025-08-24T16:47:14.880151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:15.154311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 551128
94.9%
1 29884
 
5.1%

Wilderness_Area3
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
327648 
1
253364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Length

2025-08-24T16:47:15.534970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:15.969805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring characters

ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 327648
56.4%
1 253364
43.6%

Wilderness_Area4
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
544044 
1
 
36968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Length

2025-08-24T16:47:16.595256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:16.863277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 544044
93.6%
1 36968
 
6.4%

Soil_Type1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.6 MiB
positive
577981 
negative
 
3031

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4648096
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpositive
2nd rowpositive
3rd rowpositive
4th rowpositive
5th rowpositive

Common Values

ValueCountFrequency (%)
positive 577981
99.5%
negative 3031
 
0.5%

Length

2025-08-24T16:47:17.666137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:18.808472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
positive 577981
99.5%
negative 3031
 
0.5%

Most occurring characters

ValueCountFrequency (%)
i 1158993
24.9%
e 584043
12.6%
t 581012
12.5%
v 581012
12.5%
p 577981
12.4%
o 577981
12.4%
s 577981
12.4%
n 3031
 
0.1%
g 3031
 
0.1%
a 3031
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4648096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1158993
24.9%
e 584043
12.6%
t 581012
12.5%
v 581012
12.5%
p 577981
12.4%
o 577981
12.4%
s 577981
12.4%
n 3031
 
0.1%
g 3031
 
0.1%
a 3031
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4648096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1158993
24.9%
e 584043
12.6%
t 581012
12.5%
v 581012
12.5%
p 577981
12.4%
o 577981
12.4%
s 577981
12.4%
n 3031
 
0.1%
g 3031
 
0.1%
a 3031
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4648096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1158993
24.9%
e 584043
12.6%
t 581012
12.5%
v 581012
12.5%
p 577981
12.4%
o 577981
12.4%
s 577981
12.4%
n 3031
 
0.1%
g 3031
 
0.1%
a 3031
 
0.1%

Soil_Type2
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
573487 
1
 
7525

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Length

2025-08-24T16:47:19.476155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:19.763824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 573487
98.7%
1 7525
 
1.3%

Soil_Type3
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
576189 
1
 
4823

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Length

2025-08-24T16:47:20.149965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:20.419063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 576189
99.2%
1 4823
 
0.8%

Soil_Type4
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
568616 
1
 
12396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Length

2025-08-24T16:47:20.802708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:21.091969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 568616
97.9%
1 12396
 
2.1%

Soil_Type5
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
579415 
1
 
1597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Length

2025-08-24T16:47:21.497496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:21.780634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579415
99.7%
1 1597
 
0.3%

Soil_Type6
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
574437 
1
 
6575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Length

2025-08-24T16:47:22.204288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:22.479777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 574437
98.9%
1 6575
 
1.1%

Soil_Type7
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580907 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Length

2025-08-24T16:47:22.840892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:23.103290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580907
> 99.9%
1 105
 
< 0.1%

Soil_Type8
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580833 
1
 
179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Length

2025-08-24T16:47:23.485232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:23.660200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580833
> 99.9%
1 179
 
< 0.1%

Soil_Type9
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
579865 
1
 
1147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Length

2025-08-24T16:47:23.894006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:24.150163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579865
99.8%
1 1147
 
0.2%

Soil_Type10
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
548378 
1
 
32634

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Length

2025-08-24T16:47:24.521208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:24.784310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 548378
94.4%
1 32634
 
5.6%

Soil_Type11
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
568602 
1
 
12410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Length

2025-08-24T16:47:25.143623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:25.335038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 568602
97.9%
1 12410
 
2.1%

Soil_Type12
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
551041 
1
 
29971

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Length

2025-08-24T16:47:25.572075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:25.826442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 551041
94.8%
1 29971
 
5.2%

Soil_Type13
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
563581 
1
 
17431

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Length

2025-08-24T16:47:26.199028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:26.456448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 563581
97.0%
1 17431
 
3.0%

Soil_Type14
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580413 
1
 
599

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Length

2025-08-24T16:47:26.851581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:27.110218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580413
99.9%
1 599
 
0.1%

Soil_Type15
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
581009 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Length

2025-08-24T16:47:27.492967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:27.679509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 581009
> 99.9%
1 3
 
< 0.1%

Soil_Type16
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
578167 
1
 
2845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Length

2025-08-24T16:47:27.928588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:28.108409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 578167
99.5%
1 2845
 
0.5%

Soil_Type17
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
577590 
1
 
3422

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Length

2025-08-24T16:47:28.352938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:28.614188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 577590
99.4%
1 3422
 
0.6%

Soil_Type18
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
579113 
1
 
1899

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Length

2025-08-24T16:47:28.966702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:29.136874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579113
99.7%
1 1899
 
0.3%

Soil_Type19
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
576991 
1
 
4021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Length

2025-08-24T16:47:29.380950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:29.641760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 576991
99.3%
1 4021
 
0.7%

Soil_Type20
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
571753 
1
 
9259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Length

2025-08-24T16:47:30.194957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:30.459667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 571753
98.4%
1 9259
 
1.6%

Soil_Type21
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580174 
1
 
838

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Length

2025-08-24T16:47:30.828872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:31.008035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580174
99.9%
1 838
 
0.1%

Soil_Type22
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
547639 
1
 
33373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Length

2025-08-24T16:47:31.246640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:31.512547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 547639
94.3%
1 33373
 
5.7%

Soil_Type23
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
523260 
1
57752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Length

2025-08-24T16:47:31.906500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:32.345948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 523260
90.1%
1 57752
 
9.9%

Soil_Type24
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
559734 
1
 
21278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Length

2025-08-24T16:47:32.963149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:33.225751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 559734
96.3%
1 21278
 
3.7%

Soil_Type25
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580538 
1
 
474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Length

2025-08-24T16:47:33.617329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:33.787237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580538
99.9%
1 474
 
0.1%

Soil_Type26
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
578423 
1
 
2589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Length

2025-08-24T16:47:34.018106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:34.302882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 578423
99.6%
1 2589
 
0.4%

Soil_Type27
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
579926 
1
 
1086

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Length

2025-08-24T16:47:34.678802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:34.863407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579926
99.8%
1 1086
 
0.2%

Soil_Type28
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580066 
1
 
946

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Length

2025-08-24T16:47:35.114443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:35.293458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580066
99.8%
1 946
 
0.2%

Soil_Type29
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
465765 
1
115247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Length

2025-08-24T16:47:35.531247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:35.956137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 465765
80.2%
1 115247
 
19.8%

Soil_Type30
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
550842 
1
 
30170

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Length

2025-08-24T16:47:36.555218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:36.733251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 550842
94.8%
1 30170
 
5.2%

Soil_Type31
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
555346 
1
 
25666

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Length

2025-08-24T16:47:36.971675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:37.241077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 555346
95.6%
1 25666
 
4.4%

Soil_Type32
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
528493 
1
 
52519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Length

2025-08-24T16:47:37.597972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:38.059940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 528493
91.0%
1 52519
 
9.0%

Soil_Type33
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
535858 
1
 
45154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Length

2025-08-24T16:47:38.663431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:38.916463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 535858
92.2%
1 45154
 
7.8%

Soil_Type34
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
579401 
1
 
1611

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Length

2025-08-24T16:47:39.273046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:39.439053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579401
99.7%
1 1611
 
0.3%

Soil_Type35
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
579121 
1
 
1891

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Length

2025-08-24T16:47:39.667007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:39.838447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579121
99.7%
1 1891
 
0.3%

Soil_Type36
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580893 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Length

2025-08-24T16:47:40.069525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:40.240134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580893
> 99.9%
1 119
 
< 0.1%

Soil_Type37
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
580714 
1
 
298

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Length

2025-08-24T16:47:40.477712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:40.655219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 580714
99.9%
1 298
 
0.1%

Soil_Type38
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
565439 
1
 
15573

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Length

2025-08-24T16:47:40.894923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:41.151821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 565439
97.3%
1 15573
 
2.7%

Soil_Type39
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
567206 
1
 
13806

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Length

2025-08-24T16:47:41.506134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:41.755846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 567206
97.6%
1 13806
 
2.4%

Soil_Type40
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
0
572262 
1
 
8750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Length

2025-08-24T16:47:42.106578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:42.361627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 572262
98.5%
1 8750
 
1.5%

Water_Level
Real number (ℝ)

Uniform  Unique 

Distinct581012
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290507.5
Minimum2
Maximum581013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:42.959914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile29052.55
Q1145254.75
median290507.5
Q3435760.25
95-th percentile551962.45
Maximum581013
Range581011
Interquartile range (IQR)290505.5

Descriptive statistics

Standard deviation167723.86
Coefficient of variation (CV)0.57734778
Kurtosis-1.2
Mean290507.5
Median Absolute Deviation (MAD)145253
Skewness-1.6017226 × 10-15
Sum1.6878834 × 1011
Variance2.8131294 × 1010
MonotonicityStrictly increasing
2025-08-24T16:47:43.206733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
< 0.1%
387347 1
 
< 0.1%
387341 1
 
< 0.1%
387342 1
 
< 0.1%
387343 1
 
< 0.1%
387344 1
 
< 0.1%
387345 1
 
< 0.1%
387346 1
 
< 0.1%
387348 1
 
< 0.1%
387339 1
 
< 0.1%
Other values (581002) 581002
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
581013 1
< 0.1%
581012 1
< 0.1%
581011 1
< 0.1%
581010 1
< 0.1%
581009 1
< 0.1%
581008 1
< 0.1%
581007 1
< 0.1%
581006 1
< 0.1%
581005 1
< 0.1%
581004 1
< 0.1%

Observation_ID
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.7 MiB
1
581012 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 581012
100.0%

Length

2025-08-24T16:47:43.338800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-24T16:47:43.503244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 581012
100.0%

Most occurring characters

ValueCountFrequency (%)
1 581012
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 581012
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 581012
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 581012
100.0%

Cover_Type
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0514705
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-08-24T16:47:43.874279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3965043
Coefficient of variation (CV)0.6807333
Kurtosis4.9489652
Mean2.0514705
Median Absolute Deviation (MAD)1
Skewness2.2765737
Sum1191929
Variance1.9502243
MonotonicityNot monotonic
2025-08-24T16:47:44.225557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 283301
48.8%
1 211840
36.5%
3 35754
 
6.2%
7 20510
 
3.5%
6 17367
 
3.0%
5 9493
 
1.6%
4 2747
 
0.5%
ValueCountFrequency (%)
1 211840
36.5%
2 283301
48.8%
3 35754
 
6.2%
4 2747
 
0.5%
5 9493
 
1.6%
6 17367
 
3.0%
7 20510
 
3.5%
ValueCountFrequency (%)
7 20510
 
3.5%
6 17367
 
3.0%
5 9493
 
1.6%
4 2747
 
0.5%
3 35754
 
6.2%
2 283301
48.8%
1 211840
36.5%

Interactions

2025-08-24T16:46:31.318524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:41.552223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:01.212672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:03.686468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:06.095160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:08.541351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:11.414945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:13.970494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:16.523172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:18.979564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:21.587835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:23.941595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:26.354013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:28.874226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:31.491406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:43.497564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:01.401210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:03.841578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:06.265936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:08.734032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:11.596333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:14.143140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:16.698192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:19.146252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:21.747997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:24.109881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:26.540679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:29.056998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:31.661367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:45.318356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:01.572991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:04.028690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:06.428835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:08.910465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:11.761625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:14.309246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:16.869063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:19.302297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:21.906057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:24.272466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:26.713814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:29.227347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:31.825816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:46.665655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:01.739978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:04.210803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:06.582100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:09.085551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:11.961224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:14.478061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:17.041934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:19.470800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:22.067213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:24.440676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:26.887406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:29.399620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:32.000027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:48.748743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:01.923980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:04.382502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:06.753174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:09.261906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:12.137033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:14.665871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:17.217431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:19.647647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:22.237787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:24.613810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:27.076594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:29.591383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:32.179866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:51.474649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:02.110482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:04.547383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:06.970709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:09.455316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:12.359677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:14.913317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:17.402111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:19.830950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:22.418498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:24.800562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:27.271184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:29.785347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:32.588784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:52.914266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:02.303113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:04.722091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:07.150170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:09.649443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:12.547575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:15.093486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:17.595882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:20.014845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:22.603834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:25.002528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:27.468488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:29.966014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:32.769404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:54.345645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:02.485384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:04.900768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:07.320828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:09.869636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:12.731172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:15.290381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:17.778119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:20.190931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:22.783500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:25.178000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:27.644944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:30.147050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:32.938946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:55.651893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:02.657506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:05.075419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:07.486569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:10.064942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:12.900716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:15.471024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:17.946545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:20.351789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:22.951132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:25.344679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:27.828788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:30.321552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:33.101957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:56.035362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:02.818545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:05.249991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:07.649729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:10.240084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:13.072681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:15.636421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:18.114696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:20.541812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:23.105250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:25.502496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:28.003595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:30.485089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:33.273342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:56.436162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:02.995242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:05.412970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:07.818588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:10.415920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:13.239404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:15.807394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:18.284773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:20.700886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:23.260390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:25.655458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:28.178793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:30.647049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:33.441700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:56.841915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:03.171295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:05.590994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:08.007073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:10.586987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:13.409998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:15.981183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:18.460392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:20.859159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:23.422669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:25.819135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:28.355048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:30.817709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:33.619780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:57.930314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:03.350397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:05.763884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:08.181250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:11.051567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:13.594077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:16.168611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:18.631939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:21.256843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:23.601362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:26.003008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:28.525660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:30.987269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:33.783667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:45:59.909194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:03.521219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:05.939190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:08.350266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:11.226138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:13.790235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:16.347706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:18.816814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:21.420145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:23.771017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:26.178797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:28.704541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-24T16:46:31.154996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-08-24T16:47:49.922300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AspectCover_TypeElevationFacetHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysInclinationSlopeSoil_Type1Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type2Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type3Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type4Soil_Type40Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Vertical_Distance_To_HydrologyWater_LevelWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4
Aspect1.0000.0250.0441.0000.641-0.4290.421-0.1130.0050.0190.0010.0720.0480.1590.0780.0940.1730.0130.0040.0190.0180.0460.0110.0640.0450.0480.0420.0280.1590.0590.0740.0670.0690.1110.1060.1320.0920.0950.0660.0610.0300.0370.0270.0320.0460.1770.0340.0260.0260.0070.0040.0310.0730.0010.2090.0710.1910.130
Cover_Type0.0251.000-0.4910.025-0.0360.013-0.034-0.137-0.028-0.2220.0010.1510.2240.4710.1090.1980.1380.1570.0130.0370.1930.0590.0480.3100.0450.0470.2120.1870.0840.0150.0530.0200.0370.1970.3260.1480.0800.1070.0940.0430.1490.0380.1180.3410.3310.3420.2660.1630.2820.0130.0100.0330.096-0.0020.3110.1500.1120.736
Elevation0.044-0.4911.0000.0440.0730.0150.1500.1550.2870.3420.001-0.1600.3640.5140.2180.2780.1490.1290.0160.0700.1320.1900.0680.2540.0880.0240.2240.1930.0880.0360.0710.0410.0620.2380.2270.1230.0930.1900.0980.0330.1360.0400.0670.3860.3130.2390.6220.2880.3110.0200.0260.1530.087-0.0000.2790.2980.1990.881
Facet1.0000.0250.0441.0000.641-0.4290.421-0.1130.0050.0190.0010.0720.0480.1590.0780.0940.1730.0130.0040.0190.0180.0460.0110.0640.0450.0480.0420.0280.1590.0590.0740.0670.0690.1110.1060.1320.0920.0950.0660.0610.0300.0370.0270.0320.0460.1770.0330.0260.0260.0070.0040.0310.0730.0010.2090.0710.1910.130
Hillshade_3pm0.641-0.0360.0730.6411.000-0.8230.574-0.0830.0380.1020.000-0.1730.1480.1330.0860.1390.2030.0090.0000.0360.0380.0500.0580.0190.0680.0460.0460.1410.0280.0360.0330.0550.1730.0920.1260.1320.0570.1260.1270.0270.0300.0130.0240.0570.0870.0550.0400.0590.0160.0110.0160.0270.038-0.0000.1870.0550.1150.186
Hillshade_9am-0.4290.0130.015-0.429-0.8231.000-0.1010.124-0.042-0.0100.001-0.1310.0480.2560.0570.1190.1240.0120.0000.0250.0250.0350.0540.0370.0550.0310.0410.1230.1220.0400.0380.0470.1520.0840.0560.1380.0830.0980.1000.0190.0290.0090.0120.0390.0700.0480.0170.0710.0240.0110.0170.026-0.1290.0000.2110.0280.1320.257
Hillshade_Noon0.421-0.0340.1500.4210.574-0.1011.0000.0170.0280.1740.001-0.4340.0900.2500.0740.0810.0840.0000.0010.0180.0300.0220.0490.0420.0360.0320.0390.1310.1410.0100.0330.0240.0080.0800.0360.0430.0510.1210.1290.0380.0150.0160.0240.0460.0720.0780.0410.0790.0170.0060.0140.013-0.0960.0000.0990.0490.0670.220
Horizontal_Distance_To_Fire_Points-0.113-0.1370.155-0.113-0.0830.1240.0171.0000.0740.3220.001-0.1700.1170.2090.0780.2960.1110.0510.0040.1030.0400.1570.0250.0970.1220.0300.0540.0790.0700.0740.0730.0440.0380.2160.1000.0740.0780.1060.0910.0460.0500.0210.0350.0930.0440.0870.0640.0660.1080.0820.0420.052-0.0430.0000.3810.1130.3100.317
Horizontal_Distance_To_Hydrology0.005-0.0280.2870.0050.038-0.0420.0280.0741.0000.047-0.0010.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0030.0000.0030.0000.0040.0040.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.6190.0010.0030.0000.0030.000
Horizontal_Distance_To_Roadways0.019-0.2220.3420.0190.102-0.0100.1740.3220.0471.000-0.000-0.2050.1320.2040.1070.0990.1200.0450.0040.0550.0590.0730.0920.0930.0890.0370.1330.0500.0680.0730.0780.0500.0710.3310.1010.1210.1450.1580.1360.0460.0510.0330.0470.0910.1110.0950.0690.0990.1410.0540.0470.062-0.038-0.0010.4830.2260.3990.357
Inclination0.0010.0010.0010.0010.0000.0010.0010.001-0.001-0.0001.000-0.0010.0000.0000.0020.0000.0000.0010.0000.0000.0000.0000.0010.0000.0030.0000.0000.0000.0000.0000.0040.0000.0040.0000.0000.0030.0020.0010.0010.0020.0010.0010.0020.0020.0040.0000.0000.0000.0030.0040.0000.000-0.001-0.0000.0000.0000.0000.000
Slope0.0720.151-0.1600.072-0.173-0.131-0.434-0.1700.019-0.205-0.0011.0000.1220.2470.0710.1760.2010.0010.0000.0370.0440.0480.1060.0210.0790.0260.0570.2120.1140.0360.0450.0510.0900.0940.1440.0810.0830.1400.2310.0130.0240.0150.0090.0740.0960.1380.0310.0850.0120.0200.0350.0340.301-0.0010.2310.0410.1480.276
Soil_Type10.0480.2240.3640.0480.1480.0480.0900.1170.0000.1320.0000.1221.0000.0180.0110.0170.0130.0010.0000.0050.0050.0040.0060.0080.0090.0020.0180.0240.0140.0010.0040.0030.0020.0360.0060.0170.0150.0230.0210.0030.0040.0000.0000.0120.0110.0110.0090.0030.0080.0000.0000.0030.0230.0000.0650.0170.0640.278
Soil_Type100.1590.4710.5140.1590.1330.2560.2500.2090.0000.2040.0000.2470.0181.0000.0360.0570.0430.0080.0000.0170.0190.0140.0200.0280.0310.0090.0600.0810.0480.0070.0160.0100.0100.1210.0220.0570.0520.0770.0710.0130.0140.0030.0050.0400.0380.0360.0300.0130.0260.0030.0040.0110.0670.0030.2200.0570.0070.485
Soil_Type110.0780.1090.2180.0780.0860.0570.0740.0780.0000.1070.0020.0710.0110.0361.0000.0340.0260.0040.0000.0100.0110.0080.0120.0170.0190.0050.0360.0490.0290.0040.0100.0060.0060.0730.0130.0350.0320.0470.0430.0080.0080.0010.0030.0240.0230.0220.0180.0080.0160.0010.0020.0060.0300.0000.1330.0340.1540.009
Soil_Type120.0940.1980.2780.0940.1390.1190.0810.2960.0000.0990.0000.1760.0170.0570.0341.0000.0410.0070.0000.0160.0180.0130.0190.0270.0300.0090.0580.0770.0450.0060.0150.0100.0090.1160.0210.0550.0500.0730.0680.0120.0130.0030.0050.0390.0360.0340.0290.0120.0250.0030.0040.0100.0710.0010.2580.0540.2050.061
Soil_Type130.1730.1380.1490.1730.2030.1240.0840.1110.0000.1200.0000.2010.0130.0430.0260.0411.0000.0050.0000.0120.0130.0100.0150.0200.0220.0060.0430.0580.0340.0050.0120.0070.0070.0870.0160.0410.0380.0550.0510.0090.0100.0020.0040.0290.0270.0260.0220.0090.0190.0010.0020.0080.0870.0000.1590.0290.1950.046
Soil_Type140.0130.1570.1290.0130.0090.0120.0000.0510.0000.0450.0010.0010.0010.0080.0040.0070.0051.0000.0000.0010.0020.0000.0020.0030.0040.0000.0080.0110.0060.0000.0010.0000.0000.0160.0020.0070.0070.0100.0090.0000.0000.0000.0000.0050.0050.0040.0040.0000.0030.0000.0000.0000.0220.0000.0290.0070.0020.070
Soil_Type150.0040.0130.0160.0040.0000.0000.0010.0040.0000.0040.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.007
Soil_Type160.0190.0370.0700.0190.0360.0250.0180.1030.0000.0550.0000.0370.0050.0170.0100.0160.0120.0010.0001.0000.0050.0040.0060.0080.0090.0020.0170.0230.0140.0010.0040.0020.0020.0350.0060.0160.0150.0220.0200.0030.0040.0000.0000.0110.0110.0100.0080.0030.0070.0000.0000.0030.0470.0000.0430.0030.0450.008
Soil_Type170.0180.1930.1320.0180.0380.0250.0300.0400.0000.0590.0000.0440.0050.0190.0110.0180.0130.0020.0000.0051.0000.0040.0060.0090.0100.0020.0190.0260.0150.0010.0050.0030.0030.0380.0070.0180.0160.0240.0220.0040.0040.0000.0000.0130.0120.0110.0090.0040.0080.0000.0000.0030.0490.0000.0690.0180.0520.053
Soil_Type180.0460.0590.1900.0460.0500.0350.0220.1570.0000.0730.0000.0480.0040.0140.0080.0130.0100.0000.0000.0040.0041.0000.0040.0060.0070.0010.0140.0190.0110.0000.0030.0020.0010.0280.0050.0130.0120.0180.0170.0020.0030.0000.0000.0090.0090.0080.0070.0020.0060.0000.0000.0020.0390.0010.0590.0030.0500.015
Soil_Type190.0110.0480.0680.0110.0580.0540.0490.0250.0000.0920.0010.1060.0060.0200.0120.0190.0150.0020.0000.0060.0060.0041.0000.0090.0100.0030.0210.0280.0160.0020.0050.0030.0030.0410.0070.0190.0180.0260.0240.0040.0040.0000.0010.0140.0130.0120.0100.0040.0090.0000.0000.0030.0510.0020.0390.0370.0450.022
Soil_Type20.0640.3100.2540.0640.0190.0370.0420.0970.0000.0930.0000.0210.0080.0280.0170.0270.0200.0030.0000.0080.0090.0060.0091.0000.0140.0040.0280.0380.0220.0030.0070.0050.0040.0570.0100.0270.0250.0360.0330.0060.0060.0000.0020.0190.0180.0170.0140.0060.0120.0000.0010.0050.0300.0040.1030.0270.0640.104
Soil_Type200.0450.0450.0880.0450.0680.0550.0360.1220.0000.0890.0030.0790.0090.0310.0190.0300.0220.0040.0000.0090.0100.0070.0100.0141.0000.0040.0310.0420.0250.0030.0080.0050.0050.0630.0110.0300.0270.0400.0370.0060.0070.0000.0020.0210.0200.0190.0160.0060.0130.0000.0010.0050.0660.0030.0720.0260.0440.033
Soil_Type210.0480.0470.0240.0480.0460.0310.0320.0300.0000.0370.0000.0260.0020.0090.0050.0090.0060.0000.0000.0020.0020.0010.0030.0040.0041.0000.0090.0120.0070.0000.0020.0000.0000.0190.0030.0090.0080.0120.0110.0010.0010.0000.0000.0060.0060.0050.0040.0010.0040.0000.0000.0000.0240.0040.0340.0090.0430.010
Soil_Type220.0420.2120.2240.0420.0460.0410.0390.0540.0000.1330.0000.0570.0180.0600.0360.0580.0430.0080.0000.0170.0190.0140.0210.0280.0310.0091.0000.0820.0480.0070.0160.0110.0100.1230.0230.0580.0530.0780.0720.0130.0140.0030.0050.0410.0380.0360.0300.0130.0260.0030.0040.0110.0760.0000.0690.1220.0920.064
Soil_Type230.0280.1870.1930.0280.1410.1230.1310.0790.0020.0500.0000.2120.0240.0810.0490.0770.0580.0110.0000.0230.0260.0190.0280.0380.0420.0120.0821.0000.0650.0090.0220.0140.0130.1650.0300.0780.0710.1050.0960.0170.0190.0040.0070.0550.0520.0490.0410.0170.0350.0040.0060.0150.1620.0020.0300.1350.0480.087
Soil_Type240.1590.0840.0880.1590.0280.1220.1410.0700.0000.0680.0000.1140.0140.0480.0290.0450.0340.0060.0000.0140.0150.0110.0160.0220.0250.0070.0480.0651.0000.0050.0130.0080.0080.0970.0180.0460.0420.0610.0570.0100.0110.0020.0040.0320.0300.0290.0240.0100.0210.0020.0030.0080.0410.0000.1220.0430.1290.051
Soil_Type250.0590.0150.0360.0590.0360.0400.0100.0740.0000.0730.0000.0360.0010.0070.0040.0060.0050.0000.0000.0010.0010.0000.0020.0030.0030.0000.0070.0090.0051.0000.0010.0000.0000.0140.0020.0060.0060.0090.0080.0000.0000.0000.0000.0040.0040.0040.0030.0000.0020.0000.0000.0000.1390.0020.0260.1230.0250.007
Soil_Type260.0740.0530.0710.0740.0330.0380.0330.0730.0000.0780.0040.0450.0040.0160.0100.0150.0120.0010.0000.0040.0050.0030.0050.0070.0080.0020.0160.0220.0130.0011.0000.0020.0020.0330.0060.0160.0140.0210.0190.0030.0030.0000.0000.0110.0100.0100.0080.0030.0070.0000.0000.0020.0220.0020.0600.0150.0760.017
Soil_Type270.0670.0200.0410.0670.0550.0470.0240.0440.0000.0500.0000.0510.0030.0100.0060.0100.0070.0000.0000.0020.0030.0020.0030.0050.0050.0000.0110.0140.0080.0000.0021.0000.0000.0210.0040.0100.0090.0140.0120.0010.0020.0000.0000.0070.0060.0060.0050.0010.0040.0000.0000.0010.1170.0000.0390.0100.0490.011
Soil_Type280.0690.0370.0620.0690.1730.1520.0080.0380.0000.0710.0040.0900.0020.0100.0060.0090.0070.0000.0000.0020.0030.0010.0030.0040.0050.0000.0100.0130.0080.0000.0020.0001.0000.0200.0030.0090.0080.0130.0120.0010.0010.0000.0000.0060.0060.0060.0050.0010.0040.0000.0000.0000.1230.0020.0360.0090.0460.010
Soil_Type290.1110.1970.2380.1110.0920.0840.0800.2160.0030.3310.0000.0940.0360.1210.0730.1160.0870.0160.0000.0350.0380.0280.0410.0570.0630.0190.1230.1650.0970.0140.0330.0210.0201.0000.0450.1160.1070.1570.1440.0260.0280.0070.0110.0830.0780.0730.0610.0260.0530.0060.0090.0220.0900.0010.5510.1140.4370.130
Soil_Type30.1060.3260.2270.1060.1260.0560.0360.1000.0000.1010.0000.1440.0060.0220.0130.0210.0160.0020.0000.0060.0070.0050.0070.0100.0110.0030.0230.0300.0180.0020.0060.0040.0030.0451.0000.0210.0200.0290.0260.0040.0050.0000.0010.0150.0140.0130.0110.0040.0100.0000.0000.0040.0350.0020.0830.0210.0100.167
Soil_Type300.1320.1480.1230.1320.1320.1380.0430.0740.0030.1210.0030.0810.0170.0570.0350.0550.0410.0070.0000.0160.0180.0130.0190.0270.0300.0090.0580.0780.0460.0060.0160.0100.0090.1160.0211.0000.0500.0740.0680.0120.0130.0030.0050.0390.0360.0350.0290.0120.0250.0030.0040.0100.0240.0000.2590.0540.2060.061
Soil_Type310.0920.0800.0930.0920.0570.0830.0510.0780.0000.1450.0020.0830.0150.0520.0320.0500.0380.0070.0000.0150.0160.0120.0180.0250.0270.0080.0530.0710.0420.0060.0140.0090.0080.1070.0200.0501.0000.0680.0620.0110.0120.0020.0040.0360.0330.0320.0270.0110.0230.0020.0030.0090.0440.0030.1940.0340.2370.056
Soil_Type320.0950.1070.1900.0950.1260.0980.1210.1060.0040.1580.0010.1400.0230.0770.0470.0730.0550.0100.0000.0220.0240.0180.0260.0360.0400.0120.0780.1050.0610.0090.0210.0140.0130.1570.0290.0740.0681.0000.0910.0170.0180.0040.0070.0520.0490.0470.0390.0160.0340.0040.0050.0140.0560.0020.2840.0290.3130.082
Soil_Type330.0660.0940.0980.0660.1270.1000.1290.0910.0040.1360.0010.2310.0210.0710.0430.0680.0510.0090.0000.0200.0220.0170.0240.0330.0370.0110.0720.0960.0570.0080.0190.0120.0120.1440.0260.0680.0620.0911.0000.0150.0160.0040.0060.0480.0450.0430.0360.0150.0310.0030.0050.0130.1660.0000.2620.0140.2940.076
Soil_Type340.0610.0430.0330.0610.0270.0190.0380.0460.0000.0460.0020.0130.0030.0130.0080.0120.0090.0000.0000.0030.0040.0020.0040.0060.0060.0010.0130.0170.0100.0000.0030.0010.0010.0260.0040.0120.0110.0170.0151.0000.0020.0000.0000.0090.0080.0080.0060.0020.0050.0000.0000.0010.0750.0040.0480.0120.0600.014
Soil_Type350.0300.1490.1360.0300.0300.0290.0150.0500.0000.0510.0010.0240.0040.0140.0080.0130.0100.0000.0000.0040.0040.0030.0040.0060.0070.0010.0140.0190.0110.0000.0030.0020.0010.0280.0050.0130.0120.0180.0160.0021.0000.0000.0000.0090.0090.0080.0070.0020.0060.0000.0000.0020.0200.0000.0120.0550.0050.015
Soil_Type360.0370.0380.0400.0370.0130.0090.0160.0210.0000.0330.0010.0150.0000.0030.0010.0030.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0070.0000.0030.0020.0040.0040.0000.0001.0000.0000.0020.0010.0010.0000.0000.0000.0000.0000.0000.0220.0000.0130.0030.0160.003
Soil_Type370.0270.1180.0670.0270.0240.0120.0240.0350.0000.0470.0020.0090.0000.0050.0030.0050.0040.0000.0000.0000.0000.0000.0010.0020.0020.0000.0050.0070.0040.0000.0000.0000.0000.0110.0010.0050.0040.0070.0060.0000.0000.0001.0000.0030.0030.0030.0020.0000.0020.0000.0000.0000.0050.0000.0150.0050.0100.006
Soil_Type380.0320.3410.3860.0320.0570.0390.0460.0930.0000.0910.0020.0740.0120.0400.0240.0390.0290.0050.0000.0110.0130.0090.0140.0190.0210.0060.0410.0550.0320.0040.0110.0070.0060.0830.0150.0390.0360.0520.0480.0090.0090.0020.0031.0000.0260.0240.0200.0090.0180.0010.0020.0070.0200.0030.0110.0610.0170.043
Soil_Type390.0460.3310.3130.0460.0870.0700.0720.0440.0010.1110.0040.0960.0110.0380.0230.0360.0270.0050.0000.0110.0120.0090.0130.0180.0200.0060.0380.0520.0300.0040.0100.0060.0060.0780.0140.0360.0330.0490.0450.0080.0090.0010.0030.0261.0000.0230.0190.0080.0170.0010.0020.0070.0540.0000.0130.0110.0020.041
Soil_Type40.1770.3420.2390.1770.0550.0480.0780.0870.0000.0950.0000.1380.0110.0360.0220.0340.0260.0040.0000.0100.0110.0080.0120.0170.0190.0050.0360.0490.0290.0040.0100.0060.0060.0730.0130.0350.0320.0470.0430.0080.0080.0010.0030.0240.0231.0000.0180.0080.0160.0010.0020.0060.0350.0020.1330.0340.1380.022
Soil_Type400.0340.2660.6220.0330.0400.0170.0410.0640.0000.0690.0000.0310.0090.0300.0180.0290.0220.0040.0000.0080.0090.0070.0100.0140.0160.0040.0300.0410.0240.0030.0080.0050.0050.0610.0110.0290.0270.0390.0360.0060.0070.0000.0020.0200.0190.0181.0000.0060.0130.0000.0010.0050.2140.0010.0120.1050.0430.032
Soil_Type50.0260.1630.2880.0260.0590.0710.0790.0660.0000.0990.0000.0850.0030.0130.0080.0120.0090.0000.0000.0030.0040.0020.0040.0060.0060.0010.0130.0170.0100.0000.0030.0010.0010.0260.0040.0120.0110.0160.0150.0020.0020.0000.0000.0090.0080.0080.0061.0000.0050.0000.0000.0010.0340.0020.0470.0120.0460.201
Soil_Type60.0260.2820.3110.0260.0160.0240.0170.1080.0000.1410.0030.0120.0080.0260.0160.0250.0190.0030.0000.0070.0080.0060.0090.0120.0130.0040.0260.0350.0210.0020.0070.0040.0040.0530.0100.0250.0230.0340.0310.0050.0060.0000.0020.0180.0170.0160.0130.0051.0000.0000.0010.0040.0470.0000.0970.0250.0940.410
Soil_Type70.0070.0130.0200.0070.0110.0110.0060.0820.0000.0540.0040.0200.0000.0030.0010.0030.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0060.0000.0030.0020.0040.0030.0000.0000.0000.0000.0010.0010.0010.0000.0000.0001.0000.0000.0000.0080.0020.0150.0030.0120.003
Soil_Type80.0040.0100.0260.0040.0160.0170.0140.0420.0000.0470.0000.0350.0000.0040.0020.0040.0020.0000.0000.0000.0000.0000.0000.0010.0010.0000.0040.0060.0030.0000.0000.0000.0000.0090.0000.0040.0030.0050.0050.0000.0000.0000.0000.0020.0020.0020.0010.0000.0010.0001.0000.0000.0110.0000.0190.0040.0150.004
Soil_Type90.0310.0330.1530.0310.0270.0260.0130.0520.0000.0620.0000.0340.0030.0110.0060.0100.0080.0000.0000.0030.0030.0020.0030.0050.0050.0000.0110.0150.0080.0000.0020.0010.0000.0220.0040.0100.0090.0140.0130.0010.0020.0000.0000.0070.0070.0060.0050.0010.0040.0000.0001.0000.0280.0000.0490.0100.0390.011
Vertical_Distance_To_Hydrology0.0730.0960.0870.0730.038-0.129-0.096-0.0430.619-0.038-0.0010.3010.0230.0670.0300.0710.0870.0220.0000.0470.0490.0390.0510.0300.0660.0240.0760.1620.0410.1390.0220.1170.1230.0900.0350.0240.0440.0560.1660.0750.0200.0220.0050.0200.0540.0350.2140.0340.0470.0080.0110.0281.0000.0020.1850.0630.1450.094
Water_Level0.001-0.002-0.0000.001-0.0000.0000.0000.0000.001-0.001-0.000-0.0010.0000.0030.0000.0010.0000.0000.0000.0000.0000.0010.0020.0040.0030.0040.0000.0020.0000.0020.0020.0000.0020.0010.0020.0000.0030.0020.0000.0040.0000.0000.0000.0030.0000.0020.0010.0020.0000.0020.0000.0000.0021.0000.0040.0000.0030.000
Wilderness_Area10.2090.3110.2790.2090.1870.2110.0990.3810.0030.4830.0000.2310.0650.2200.1330.2580.1590.0290.0000.0430.0690.0590.0390.1030.0720.0340.0690.0300.1220.0260.0600.0390.0360.5510.0830.2590.1940.2840.2620.0480.0120.0130.0150.0110.0130.1330.0120.0470.0970.0150.0190.0490.1850.0041.0000.2100.7940.235
Wilderness_Area20.0710.1500.2980.0710.0550.0280.0490.1130.0000.2260.0000.0410.0170.0570.0340.0540.0290.0070.0000.0030.0180.0030.0370.0270.0260.0090.1220.1350.0430.1230.0150.0100.0090.1140.0210.0540.0340.0290.0140.0120.0550.0030.0050.0610.0110.0340.1050.0120.0250.0030.0040.0100.0630.0000.2101.0000.2050.061
Wilderness_Area30.1910.1120.1990.1910.1150.1320.0670.3100.0030.3990.0000.1480.0640.0070.1540.2050.1950.0020.0000.0450.0520.0500.0450.0640.0440.0430.0920.0480.1290.0250.0760.0490.0460.4370.0100.2060.2370.3130.2940.0600.0050.0160.0100.0170.0020.1380.0430.0460.0940.0120.0150.0390.1450.0030.7940.2051.0000.229
Wilderness_Area40.1300.7360.8810.1300.1860.2570.2200.3170.0000.3570.0000.2760.2780.4850.0090.0610.0460.0700.0070.0080.0530.0150.0220.1040.0330.0100.0640.0870.0510.0070.0170.0110.0100.1300.1670.0610.0560.0820.0760.0140.0150.0030.0060.0430.0410.0220.0320.2010.4100.0030.0040.0110.0940.0000.2350.0610.2291.000

Missing values

2025-08-24T16:46:40.190718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-24T16:46:55.863016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectFacetSlopeInclinationHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Water_LevelObservation_IDCover_Type
03208920166415.3947276.00.69162840862313722524215123661000positive000000000010000000000000000000000000000212
12789020137343.3021869.00.621245302104023523713318040010positive001000000000000000000000000000000000000313
23384615357894.2313909.0-0.2660863621531132072251562951000positive000000000000000000001000000000000000000411
33348150148371.3469396.00.78637524750120722824014524051000positive000000000000000000000000000100000000000512
43061955124310.78343011.0-0.33568517019159523823212428371000positive000000000000000000000000000100000000000612
5290394070175.35205213.00.70832921250105023321411115050010positive000000000100000000000000000000000000000712
62751450297742.82082712.0-0.9737784274331872361894990001positive000000001000000000000000000000000000000812
73366935147367.64966715.0-0.491289150-22351023923712554091000positive000000000000000000000000000010000000000911
83152565357895.70151218.00.440584424758161892051517690010positive0000000000000000000000000000010000000001012
92891785164411.52292112.00.7995129513202923124414226590010positive0010000000000000000000000000000000000001113
ElevationAspectFacetSlopeInclinationHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Water_LevelObservation_IDCover_Type
5810023686280131328.09304815.00.16935330963228924323111420370010positive00000000000000000000000000000010000000058100411
581003327190540100.3820848.0-0.9450631342352222122213511281000positive00000000000000000000000000010000000000058100512
581004359125042105.01797211.0-0.09586515031273122221512725841000positive00000000000000000000000000010000000000058100611
5810053326050263659.16678416.0-0.075698170-12308417724620618720010positive00000000000100000000000000000000000000058100712
5810063396770305762.53962811.00.780065150-1377818823418616890010positive00000000000000000000100000000000000000058100811
5810073236545117293.27463217.00.2574136006262472229823091000positive00000000000000000000000000010000000000058100912
5810083751475321805.5017196.0-0.47976227219290620423416920591000positive00000000000000000000000000000000000001058101011
581009285532072180.4532896.00.94601800171922722813629220010positive00000000000000010000000000000000000000058101116
5810103004495100250.9019237.00.11307000206723123113312601000positive00000000000000000010000000000000000000058101212
5810113631030114285.16116919.00.285539300-467642492178735150100positive00000000000000000000000000000010000000058101312